It's the ultimate question in sales and marketing. There are a plethora of tools, data pieces, and prediction models available.
We reviewed DiscoverOrg’s survey regarding the ABM predictive buyer and here is what we found out:
Let's take a closer look at each of these results.
Only when behavioral data is paired with well-defined firmographic and demographic characteristics that match the Ideal Customer Profile can it be used to forecast future behavior.
The proper contact (at the right firm) must meet the following criteria: Any type of scoring or predictive analysis requires a fully defined corporate profile as a prerequisite. All of the other data, no matter how accurate it is at forecasting, is useless if the firm isn't a good match.
Contact data elements such as Title, Department, Job Role, and Job Level are also included in the fit criterion. A salesforce is likely to spin a lot of cycles on offers that don't end up closing without an Ideal Customer Profile that touches on these topics, as well as fit requirements at the organization level.
Opportunity Insights (Favorable conditions): A prospect may come upon a solution at just the right time. But luck has never been a smart sales approach; that's why when opportunity or "trigger" data is stacked on top of Fit and Intent data, it becomes a fully predictive element of the purchase equation.
These are the data points that show that the time is right for a change.
Intent (Implicit behavior): With a foundation of fundamental demographic and firmographic facts in place, as well as favorable conditions, purpose data becomes extremely valuable for forecasting success. The behavioral activity that connects target buyers and accounts to a solution, solution category, or related subjects is known as intent data.
Form fills, content downloads, event registrations, page views, duration on page, number of visitors from an account, and other digital footprints are examples of intent data.
In general, it is evident that predictive data is important. Only 5% of respondents indicated there was no relationship between positive sales outcomes and the availability of predictive indicators (we have so many questions for that 5%! ), implying that 95% of respondents were able to link growth to predictive indicators. Higher conversion rates of the prospect to qualified lead are frequently the most beneficial result with the strongest correlation. Surprisingly, no other good outcome (such as faster sales cycles, more wins, better sales price, or more demos) was mentioned in the majority of respondents.
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This most likely indicates a couple of crucial conclusions for sales teams:
The top four most predictive individual data points belong to the Intent category and are focused on purchasers' online research behavior - however, the majority of businesses have yet to invest in this information.
The next two most predictive individual data pieces are Opportunity-related, such as detecting RFPs and planned projects/initiatives that may or may not be accompanied by a publicly published RFP (they generally aren't).
Companies utilize the least opportunity-related data, despite the fact that having access to these two data points might help you gain a seat at the table - and if the data isn't publicly available, you'll get the first seat.
Fit criteria are the primary layer of data for evaluating prospects and accounts - basic physiological data that serves as the base for Maslow's Hierarchy of Needs for sales and marketing. If the fundamentals aren't in place – for example, selling to the incorrect department or to a department with no funding – the sale becomes an extraordinarily difficult mountain to scale.
The buyers' technology profile, which includes technologies that connect with the seller's offering and technologies that complement the seller's offering, is the other most predictive match factor. Technology characteristics are so significant that they make up four of the top seven most predictive data points.
Every day, the sales agent is on the phone, seeking to set up a meeting or clinch a transaction with real people. When a prospect quits their employment, they must connect with someone else inside the business, or their opportunity will be lost. On the other hand, as soon as a crucial function is filled, the sales professional has the opportunity to interact and connect with their new prospect.
This creates a significant potential for marketing teams to better exploit data related to staff transfers. When a target account announces significant recruiting plans or a new leader in a key role, it's a good opportunity to start marketing and raising brand awareness.
How many marketing teams are leveraging this signal to send a direct mail piece to a new executive or increase targeted advertising to fast-growing firms with major recruiting ambitions, for example? Probably not as many as they ought to be.
This may appear to show a lack of coordination between sales and marketing, but it is more likely to emphasize the varied roles of sales and marketing in the buying process. Sales teams are focused on accounts with the capacity to purchase right now, whereas marketing is focused on discovering prospects at the top of the funnel who exhibit early intent.
On the other side, it might signal that not enough marketing teams are concentrating on (or even collecting) basic Fit criteria, or explicitly targeting and enticing prospects that fit that Ideal Customer Profile. As a result, marketing-sales alignment, lead quality, and funnel conversion rates will all increase.
We asked respondents to list all of the numerous ways they use the predictive data they collect; thankfully, the majority are utilizing the data to make their sales and marketing operations smarter and more focused.
Despite the excitement around account-based marketing (ABM), few companies are really adopting ABM programs. Only 20% of respondents have adopted an ABM strategy, and only 30% rate their customers and leads based on their potential to buy.
Even more discouraging, just 28% of DiscoverOrg’s survey respondents said they use the data they acquire to cleanse or remove bad data from their systems. What happens if database health isn't prioritized? More data is coming in, more data is going bad, and more teams are feeling overwhelmed by how to make choices with all of that data, whether for ABM or not. Worse still, the decisions they do make may not always be the best ones.
Purchases are made at the intersection of accurate Fit, Intent, and Opportunity data, and the winning sales and marketing teams are those that combine this data to identify, prioritize, and monitor their target accounts and contacts, allowing them to reach out at the right time with the right message to the right person.
If you'd like to see how NNC's award-winning sales and marketing intelligence platform uses precise Fit, Intent, and Opportunity data to assist sales and marketing teams to find, prioritize, and effectively anticipate accounts most likely to purchase, schedule a demo now.